The World Wide Web represents a global, timely, and largely unregulated touchstone of popular opinion, which many believe may be exploited for early insights into new trends and opinions. Areas proposed for such analysis comprise the outcome of political elections, the emergence of the next big musical group/toy/consumer electronic device, and the pulse of the global economy. Yet, despite widely touted opinions that marketing will soon be a small branch of machine learning, there has been little work formally demonstrating connections between online content and customer behavior such as purchase decisions.
Predicting sales from indicators is an important problem in marketing and business. The very concept of creating a new product is predicated on the assumption (or rather, prediction) that someone will eventually purchase it. The same can be said for pricing, inventory planning, production capacity planning, store placement and layout, etc.
One conventional technology for predicting sales from indicators analyzes the nature of sales spikes in amazon.com sales rank data. This approach shows that two distinct types of peaks may be identified by their growth and relaxation patterns, and this approach ties these two spike types to endogenous and exogenous events. However, this approach only addresses sales rank data to determine spikes in sales.
Another conventional approach to predicting sales from online postings predicts box office proceeds of movies from opinions posted to net news. This approach utilizes the power of Internet discussion in understanding customer views of a product or brand. Yet another conventional approach examines community-created metadata on music artists, using “buzz” or discussion on blogs to predict record sales.
Although these technologies have proven to be useful, it would be desirable to present additional improvements. Conventional approaches have not addressed the use of online public discussion to predict sales of products. An increasing fraction of the global discourse is migrating online in the form of weblogs, bulletin boards, web pages, wikis, editorials, in addition to new collaborative technologies. This migration has now proceeded to the point that topics reflecting certain individual products are sufficiently popular to allow targeted online tracking of the ebb and flow of “chatter” or postings in online discussions around these topics.
What is therefore needed is a system, a service, a computer program product, and an associated method for predicting sales from online public discussions. The need for such a solution has heretofore remained unsatisfied.